Etoposide for COVID-19

The new strain of Coronaviruses (SARS-CoV-2), and the resulting Covid-19 disease has spread swiftly across the globe after its initial detection in late December 2019 in Wuhan, China, resulting in a pandemic status declaration by WHO within 3 months. Given the heavy toll of this pandemic, researchers are actively testing various strategies including new and repurposed drugs as well as vaccines. In the current brief report, we adopted a repositioning approach using insilico molecular modeling screening using FDA approved drugs with established safety profiles for potential inhibitory effects on Covid-19 virus. We started with structure based drug design by screening more than 2000 FDA approved drugsagainst Covid-19 virus main protease enzyme (Mpro) substrate-binding pocket to identify potential hits based on their binding energies, binding modes, interacting amino acids, and therapeutic indications. In addition, we elucidate preliminary pharmacophore features for candidates bound to Covid-19 virus Mpro substratebinding pocket. The top hits include anti-viral drugs such as Darunavir, Nelfinavirand Saquinavir, some of which are already being tested in Covid-19 patients. Interestingly, one of the most promising hits in our screen is the hypercholesterolemia drug Rosuvastatin. These results certainly do not confirm or indicate antiviral activity, but can rather be used as a starting point for further in vitro and in vivo testing, either individually or in combination.

<ns4:p>We prepared the three-dimensional model of the SARS-CoV-2 (aka 2019-nCoV) 3C-like protease (3CL<ns4:sup>pro</ns4:sup>) using the crystal structure of the highly similar (96% identity) ortholog from the SARS-CoV. All residues involved in the catalysis, substrate binding and dimerisation are 100% conserved. Comparison of the polyprotein PP1AB sequences showed 86% identity. The 3C-like cleavage sites on the coronaviral polyproteins are highly conserved. Based on the near-identical substrate specificities and high sequence identities, we are of the opinion that some of the previous progress of specific inhibitors development for the SARS-CoV enzyme can be conferred on its SARS-CoV-2 counterpart. With the 3CL<ns4:sup>pro</ns4:sup> molecular model, we performed virtual screening for purchasable drugs and proposed 16 candidates for consideration. Among these, the antivirals ledipasvir or velpatasvir are particularly attractive as therapeutics to combat the new coronavirus with minimal side effects, commonly fatigue and headache. The drugs Epclusa (velpatasvir/sofosbuvir) and Harvoni (ledipasvir/sofosbuvir) could be very effective owing to their dual inhibitory actions on two viral enzymes.</ns4:p>

According to the World Health Organization (WHO), in the second half of 2022, there are about 606 million confirmed cases of COVID-19 and almost 6,500,000 deaths around the world. A pandemic was declared by the WHO in March 2020 when the new coronavirus spread around the world. The short time between the first cases in Wuhan and the declaration of a pandemic initiated the search for ways to stop the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or to attempt to cure the disease COVID-19. More than ever, research groups are developing vaccines, drugs, and immunobiological compounds, and they are even trying to repurpose drugs in an increasing number of clinical trials. There are great expectations regarding the vaccine’s effectiveness for the prevention of COVID-19. However, producing sufficient doses of vaccines for the entire population and SARS-CoV-2 variants are challenges for pharmaceutical industries. On the contrary, efforts have been made to create different vaccines with different approaches so that they can be used by the entire population. Here, we summarize about 8162 clinical trials, showing a greater number of drug clinical trials in Europe and the United States and less clinical trials in low-income countries. Promising results about the use of new drugs and drug repositioning, monoclonal antibodies, convalescent plasma, and mesenchymal stem cells to control viral infection/replication or the hyper-inflammatory response to the new coronavirus bring hope to treat the disease.

Drug repurposing requires distinguishing established drug class targets from novel molecule-specific mechanisms and rapidly derisking their therapeutic potential in a time-critical manner, particularly in a pandemic scenario. In response to the challenge to rapidly identify treatment options for COVID-19, several studies reported that statins, as a drug class, reduce mortality in these patients. However, it is unknown if different statins exhibit consistent function or may have varying therapeutic benefit. A Bayesian network tool was used to predict drugs that shift the host transcriptomic response to SARS-CoV-2 infection towards a healthy state. Drugs were predicted using 14 RNA-sequencing datasets from 72 autopsy tissues and 465 COVID-19 patient samples or from cultured human cells and organoids infected with SARS-CoV-2. Top drug predictions included statins, which were then assessed using electronic medical records containing over 4,000 COVID-19 patients on statins to determine mortality risk in patients prescribed specific statins versus untreated matched controls. The same drugs were tested in Vero E6 cells infected with SARS-CoV-2 and human endothelial cells infected with a related OC43 coronavirus. Simvastatin was among the most highly predicted compounds (14/14 datasets) and five other statins, including atorvastatin, were predicted to be active in &gt; 50% of analyses. Analysis of the clinical database revealed that reduced mortality risk was only observed in COVID-19 patients prescribed a subset of statins, including simvastatin and atorvastatin. In vitro testing of SARS-CoV-2 infected cells revealed simvastatin to be a potent direct inhibitor whereas most other statins were less effective. Simvastatin also inhibited OC43 infection and reduced cytokine production in endothelial cells. Statins may differ in their ability to sustain the lives of COVID-19 patients despite having a shared drug target and lipid-modifying mechanism of action. These findings highlight the value of target-agnostic drug prediction coupled with patient databases to identify and clinically evaluate non-obvious mechanisms and derisk and accelerate drug repurposing opportunities.

The active global SARS-CoV-2 pandemic caused more than 426 million cases and 5.8 million deaths worldwide. The development of completely new drugs for such a novel disease is a challenging, time intensive process. Despite researchers around the world working on this task, no effective treatments have been developed yet. This emphasizes the importance of drug repurposing, where treatments are found among existing drugs that are meant for different diseases. A common approach to this is based on \emph{knowledge graphs}, that condense relationships between entities like drugs, diseases and genes. Graph neural networks (GNNs) can then be used for the task at hand by predicting links in such knowledge graphs. Expanding on state-of-the-art GNN research, Doshi {\sl et al.} recently developed the \drcov \ model. We further extend their work using additional output interpretation strategies. The best aggregation strategy derives a top-100 ranking of 8,070 candidate drugs, 32 of which are currently being tested in COVID-19-related clinical trials. Moreover, we present an alternative application for the model, the generation of additional candidates based on a given pre-selection of drug candidates using collaborative filtering. In addition, we improved the implementation of the \drcov \ model by significantly shortening the inference and pre-processing time by exploiting data-parallelism. As drug repurposing is a task that requires high computation and memory resources, we further accelerate the post-processing phase using a new emerging hardware --- we propose a new approach to leverage the use of high-capacity Non-Volatile Memory for aggregate drug ranking.